Abstract: Numerous embodiments are disclosed for splitting an array of non-volatile memory cells in an analog neural memory in a deep learning artificial neural network into multiple parts. Each part of the array interacts with certain circuitry dedicated to that part and with other circuitry that is shared with one or more other parts of the array.

Abstract: Configurable input blocks and output blocks and physical layouts are disclosed for analog neural memory systems that utilize non-volatile memory cells. An input block can be configured to support different numbers of arrays arranged in a horizontal direction, and an output block can be configured to support different numbers of arrays arranged in a vertical direction. Adjustable components are disclosed for use in the configurable input blocks and output blocks. Systems and methods are utilized for compensating for leakage and offset in the input blocks and output blocks the in analog neural memory systems.

Abstract: In one example, an analog neural memory system comprises a vector-by-matrix multiplication array comprising an array of non-volatile memory cells organized into rows and columns, wherein each memory cell comprises a bit line terminal, a control gate terminal, and a word line terminal; a plurality of bit lines, wherein each of the plurality of bit lines is coupled to the bit line terminals of a column of memory cells; a plurality of control gate lines, wherein each of the plurality of control gate lines is coupled to the control gate terminals of a row of memory cells; and a plurality of word lines, wherein each of the plurality of word lines is coupled to the word line terminals of a row of memory cells; wherein the plurality of control gate lines are parallel to the plurality of bit lines and perpendicular to the plurality of word lines.

Abstract: Numerous embodiments of analog neural memory arrays are disclosed. Certain embodiments contain improved mechanisms for pulling source lines down to ground expeditiously. This is useful, for example, to minimize the voltage drop for a read, program, or erase operation.

Abstract: In one example, a system comprises an array comprising selected memory cells; an input block configured to apply, to each selected memory cell, a series of input signals to a terminal of the selected memory cell in response to a series of input bits; and an output block for generating an output of the selected memory cells, the output block comprising an analog-to-digital converter to convert current from the selected memory cells into a digital value, a shifter, an adder, and a register; wherein the shifter, adder, and register are configured to receive a series of digital values in response to the series of input bits, shift each digital value in the series of digital values based on a bit location of an input bit within the series of input bits, and add results of the shift operations to generate an output indicating values stored in the selected memory cells.

Abstract: In one example, a method comprises determining a logarithmic slope factor for a selected analog non-volatile memory cell in an array of analog non-volatile memory cells while the selected analog non-volatile memory cell is operating in a sub-threshold region; storing the logarithmic slope factor; determining a linear slope factor for the selected analog non-volatile memory cell while the selected analog non-volatile memory cell is operating in a linear region; storing the linear slope factor; and utilizing one or more of the logarithmic slope factor and the linear slope factor when programming the selected analog non-volatile memory cell to a target current.

Abstract: Numerous examples are disclosed of programming multiple rows in an array in an artificial neural network as part of a single programming operation. In one example, a method comprises ramping up an output of a high voltage generator to a first voltage level; while maintaining the output of the high voltage generator at the first voltage level, programming a plurality of words of K rows of memory cells in an array of memory cells using the output of the high voltage generator, where K>1; and after the programming, ramping down the output of the high voltage generator to a second voltage level.

Abstract: Numerous examples are disclosed of input blocks for an array of non-volatile memory cells and associated methods. In one example, a system comprises a vector-by-matrix multiplication array comprising non-volatile memory cells arranged into rows and columns; and an input block comprising a plurality of row circuits and a global digital-to-analog converter generator to generate 2m different analog voltages, where m is an integer; wherein the row circuits in the plurality of row circuits respectively apply one of the 2m different analog voltages to an associated row in the array.

Abstract: Various examples of decoders and physical layout designs for non-volatile flash memory arrays in an analog neural system are disclosed. In one example, a system comprises a plurality of vector-by-matrix multiplication arrays in an analog neural memory system, each vector-by-matrix multiplication array comprising an array of non-volatile memory cells organized into rows and columns, wherein each memory cell comprises a word line terminal; a plurality of read row decoders, each read row decoder coupled to one of the plurality of vector-by-matrix multiplication arrays for applying a voltage to one or more selected rows during a read operation; and a shared program row decoder coupled to all of the plurality of vector-by-matrix multiplication arrays for applying a voltage to one or more selected rows in one or more of the vector-by-matrix multiplication arrays during a program operation.

Abstract: In one example, a method of testing a plurality of non-volatile memory cells in an array of non-volatile memory cells, wherein the array is arranged in rows and columns, wherein each row is coupled to a word line and each column is coupled to a bit line, and wherein each word line is selectively coupled to a row decoder and each bit line is selectively coupled to a column decoder, comprises asserting, by the row decoder, all word lines in the array; asserting, by the column decoder, all bit lines in the array; performing a deep programming operation on the array of non-volatile memory cells; and measuring a total current received from the bit lines.

Abstract: In one example, a method comprises determining a program resolution current value; and setting levels for a programming operation of a plurality of non-volatile memory cells in a neural network array such that a delta current between levels of each pair of adjacent cells in the plurality is a multiple of the program resolution current value.

Abstract: Numerous examples are disclosed of a masking circuit for inputs and outputs in a neural network array. In one example, a system comprises a neural network array comprising a plurality of non-volatile memory cells arranged into rows and columns; and row circuits for respective rows in the neural network array, the row circuits comprising a masking circuit to prevent an application of a sparse input to one or more rows in the array when a condition is satisfied.

Abstract: In one example, a method comprises erasing at the same time a word of non-volatile memory cells in an array of non-volatile memory cells arranged into rows and columns, each non-volatile memory cell comprising a word line terminal, a bit line terminal, and an erase gate terminal, by turning on an erase gate enable transistor coupled to erase gate terminals of the word of non-volatile memory cells.

Abstract: Numerous embodiments for reading or verifying a value stored in a selected memory cell in a vector-by-matrix multiplication (VMM) array in an artificial neural network are disclosed. In one embodiment, an input comprises a set of input bits that result in a series of input signals applied to a terminal of the selected memory cell, further resulting in a series of output signals that are digitized, shifted based on the bit location of the corresponding input bit in the set of input bits, and added to yield an output indicating a value stored in the selected memory cell.

Abstract: In one example, a system comprises an array of non-volatile memory cells arranged into rows and columns, the array comprising a first bit line coupled to a first column of non-volatile memory cells and a second bit line coupled to a second column of non-volatile memory cells; and an output block coupled to the array, the output block comprising: a current-to-voltage converter to convert a first current on the first bit line into a first voltage and to convert a second current on the second bit line into a second voltage; and an analog-to-digital converter to convert one or more of the first voltage and the second voltage into a set of output bits.

Abstract: In one example, a system comprises a current-to-voltage converter to generate differential voltages from differential currents comprising a first current and a second current, the current-to-voltage converter comprising: a first bitline to provide the first current; a second bitline to provide the second current; a first regulator to apply a first voltage to the first bitline; a second regulator to apply a second voltage to the second bitline; a regulating circuit comprising a first input terminal, a second input terminal, a first output terminal, and a second output terminal, the first output terminal and the second output terminal providing the differential voltages; and a common mode circuit.

Abstract: In one example, a system comprises a neural network array of non-volatile memory cells arranged in rows and columns; and a logical cell comprising a first plurality of non-volatile memory cells in a first row of the array and a second plurality of non-volatile memory cells in a second row adjacent to the first row; wherein the first plurality of non-volatile memory cells and the second plurality of non-volatile memory cells are configured as one or more coarse cells and one or more fine cells.

Abstract: Numerous examples are disclosed of systems and methods to implement redundancy. In one example, a system comprises an array of non-volatile memory cells; a redundant array of non-volatile memory cells; and an input block coupled to respective rows in the array and respective rows in the redundant array and comprising row tag registers and redundant row tag registers.

Abstract: Numerous examples are disclosed of multiplexors coupled to rows in a neural network array. In one example, a system comprises a neural network array of non-volatile memory cells comprising i rows, where i is a multiple of 2; j row registers, where j<i; j digital-to-analog converters to convert j sets of digital data received from the j row registers into j analog signals; and j multiplexors to route the j analog signals to a subset of the i rows in response to a control signal.

Abstract: In one example, a non-volatile memory system, comprises an array of non-volatile memory cells arranged in rows and columns, each non-volatile memory cell comprising a source and a drain; a plurality of bit lines, each of the plurality of bit lines coupled to the drain or each non-volatile memory cell in a column of non-volatile memory cells; a source line coupled to the source of each non-volatile memory cell; and an adaptive bias decoder for providing a voltage to an erase gate line of the array during an operation, wherein the adaptive bias decoder adjusts the voltage provided to the erase gate line in response to changes in a voltage of the source line.